Nonlinear Dynamic Filtering for Image Compression

Despite advances in the domain of source coding, little recent work has been devoted to the problem of joint coding and decoding. In particular, the design of decoders has been, to our knowledge, little investigated. In this paper, we focus on image decoding and propose sufficient conditions on the design of the decoder, involving a priori assumptions on the solution and knowledge of the coder (transformation and quantization). In our approach, we propose an optimization of the reconstruction filters at the decoder to account for effects due to quantization noise. The solution can be viewed as an inverse problem with optimization of the Transform/Quantization/Decoding structure formulated using a variational approach. This leads to nonlinear filtering. Experiments using this nonlinear inverse dynamic filtering demonstrate PSNR gains over standard linear inverse filtering as well as appreciable visual improvements.